Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics
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Abstract (arXiv)
This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a model match what is reported while being arbitrarily wrong on what was claimed, which risks over-claiming advantage over classical baselines. This reflects a structural misalignment between common evaluation practices and the scientific tasks the field sets for itself. The reason is that the ground truth---exhaustive molecular dynamics on the systems of interest---is computationally costly, and this cost is what motivates the field. With ground truth inaccessible, evaluation drifts toward convenient proxies, and the resulting norms entrench easily. We identify misaligned evaluation patterns that persist as a community-level equilibrium, and propose concrete observables, reporting standards, and benchmark designs, both computational and experimental, that better reflect what GenMD aspires to deliver. Our aim is to give the field shared language and tools for evaluation that match its scientific ambitions before today's conventions calcify into norms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 63 references that are in the map. First posted on arXiv 7 Oct 2026.
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19 of 63 references.
References · 19
- 2022Torsional Diffusion for Molecular Conformer Generationcited by 263
- 2022Flow Annealed Importance Sampling Bootstrapcited by 127
- 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 110
- 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 66
- 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 51
- 2024Transferable Boltzmann Generatorscited by 46
- 2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Modelscited by 45
- 2021Deeptime: a Python library for machine learning dynamical models from time series datacited by 38
- 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generatorscited by 38
- 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 23
- 2025Temperature-Annealed Boltzmann Generatorscited by 15
- 2025Force-Free Molecular Dynamics Through Autoregressive Equivariant Networkscited by 15
7 more
- 2020Molecular Latent Space Simulatorscited by 14
- 2025Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamicscited by 11
- 2025Learning Boltzmann Generators via Constrained Mass Transportcited by 9
- 2025MD-LLM-1: A Large Language Model for Molecular Dynamicscited by 7
- 2026Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operatorscited by 4
- 2026Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularizationcited by 3
- 2026Boltzmann-Expected Molecular Design with Decoupled Annealing Flowscited by 2
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